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OBJECTIVES Our goal was to study the immediate outcome after an emergency frozen elephant trunk procedure with a Thoraflex™ Hybrid prosthesis (THP) in patients included in the EPI-Flex national registry and operated on in 21 French centres. METHODS All patients operated on in France between April 2016 and April 2019 for acute aortic syndromes and who had an frozen elephant trunk procedure with a THP were included in the study. The main end point was in-hospital mortality. The secondary end point was neuromorbidity, including paraplegia. The evolution of the main end point was monitored using a variable life-adjusted display graph with cumulative sum derivatives in order to stop inclusions in case the observed mortality became out of range compared to an expected mortality between 15% and 20%. RESULTS Enrolment ended on the scheduled date and included 109 patients. Most cases (54%) were performed at 3 centres, where more than 10 THP each were implanted (10–26). The observed mortality in the large-volume centres (22%) was comparable to that observed in the low-volume centres (20%). The individually risk-adjusted cumulative sum revealed that observed in-hospital mortality was statistically in line with that predicted by the log EuroSCORE. Analysis of the secondary end point revealed 8% cases of paraplegia, all of which appeared after treatment of the thoracic type A aortic dissection. CONCLUSIONS In France, THP for emergency frozen elephant trunk surgery outside high-volume centres did not result in excessive in-hospital deaths. However, a word of caution must be expressed regarding the prevention of medullar ischaemia even in emergency aortic surgery.

Acute aortic syndrome (AAS) comprises a complex and potentially fatal group of conditions requiring emergency specialist management. The aim of this study was to build a prediction algorithm to assist prehospital triage of AAS.Details of consecutive patients enrolled in a regional specialist aortic network were collected prospectively. Two prediction algorithms for AAS based on logistic regression and an ensemble machine learning method called SuperLearner (SL) were developed. Undertriage was defined as the proportion of patients with AAS not transported to the specialist aortic centre, and overtriage as the proportion of patients with alternative diagnoses but transported to the specialist aortic centre.Data for 976 hospital admissions between February 2010 and June 2017 were included; 609 (62·4 per cent) had AAS. Overtriage and undertriage rates were 52·3 and 16·1 per cent respectively. The population was divided into a training cohort (743 patients) and a validation cohort (233). The area under the receiver operating characteristic (ROC) curve values for the logistic regression score and the SL were 0·68 (95 per cent c.i. 0·64 to 0·72) and 0·87 (0·84 to 0·89) respectively (P 0·001) in the training cohort, and 0·67 (0·60 to 0·74) and 0·73 (0·66 to 0·79) in the validation cohort (P = 0·038). The logistic regression score was associated with undertriage and overtriage rates of 33·7 (bootstrapped 95 per cent c.i. 29·3 to 38·3) and 7·2 (4·8 to 9·8) per cent respectively, whereas the SL yielded undertriage and overtriage rates of 1·0 (0·3 to 2·0) and 30·2 (25·8 to 34·8) per cent respectively.A machine learning prediction model performed well in discriminating AAS and could be clinically useful in prehospital triage of patients with suspected AAS.Los síndromes aórticos agudos (aortic acute syndromes, AAS) constituyen un grupo complejo y potencialmente letal de entidades que requieren un tratamiento especializado en emergencias. El objetivo de este estudio fue construir un algoritmo de predicción para ayudar a la selección prehospitalaria de los AAS. MÉTODOS: Se recogieron prospectivamente una serie de pacientes consecutivos inscritos en una red regional especializada en patología aórtica. Se desarrollaron dos algoritmos de predicción para AAS basados en una regresión logística y en un método de aprendizaje automático denominado Super Learner (SL). Undertriage (infra-selección) se definió como la proporción de pacientes con AAS no transportados al centro especializado en patología aórtica y el overtriage (sobre-selección) como la proporción de pacientes con diagnósticos alternativos al AAS pero transportados al centro especializado en patología aórtica.Se incluyeron los datos de 976 ingresos hospitalarios entre febrero de 2010 y junio de 2017, con 609 (62,4%) AAS. Las tasas de overtriage y undertriage fueron del 52,3% y del 16,1%, respectivamente. La población se dividió en una cohorte de entrenamiento (n = 743) y en una cohorte de validación (n = 233). El área bajo la curva ROC para la puntuación de regresión logística y el SL fueron de 0,68 (0,64, 0,72) y de 0,87 (0,84, 0,89), respectivamente (P 0,001) en la cohorte de entrenamiento, y de 0,67 (0,60, 0,74) y de 0,73 (0,66, 0,79) en la cohorte de validación (P = 0,038). La puntuación de regresión logística se asoció con tasas de undertriage y overtriage de 33,7% (i.c. del 95% bootstrapped 29,3%, 38,3%) y de 7,2% (4,8%, 9,8%), respectivamente, mientras que el SL presentó tasas de undertriage y overtriage de 1,0% (0,3%, 2,0%) y de 30,2% (25,8%, 34,8%), respectivamente. CONCLUSIÓN: El modelo de predicción de aprendizaje automático funcionó bien para discriminar AAS y podría ser clínicamente útil en la selección prehospitalaria de pacientes con sospecha de síndrome aórtico agudo.

Purpose New biomarkers are needed to assess the vulnerability of carotid plaque to guide carotid surgery decision. Ultrafast Ultrasound Imaging (UUI) provides an estimation of plaque stiffness by Shear Wave Elastography (SWE) and the quantification of wall shear stress (WSS) by ultrafast Doppler. We aimed at evaluating the plaque stiffness and WSS applied on the plaque as potential biomarkers of plaque vulnerability. Methods Patients were referred for carotid endarterectomy because of symptomatic or asymptomatic carotid stenosis. Their plaques were evaluated by UUI within 48 hours before surgery. For each plaque, WSS and SWE were obtained on a longitudinal view at the stenosis. After endarterectomy, gross analysis and histology were performed on each removed plaque. Results 46 plaques with SWE data and 29 with WSS data were analysed. Histological analysis revealed 29 vulnerable and 17 stable plaques. Analysis of the Gray Scale Median by B mode, mean and standard deviation of stiffness by SWE were not helpful in identifying vulnerable plaques. Figure 1 presents the SWE acquisition on one plaque (A) and the distribution of plaque's stiffness average over all plaques showing the 4 ranges of stiffness (B). SWE analysis revealed that the percentage of stiffness range of [3–5] m/s was significantly increased in vulnerable plaques (p=0.048) (Figure 2A). WSS alone showed no significant difference between stable and vulnerable plaques regardless of which segment of the plaque was analysed. A multiparametric score using maximal WSS at the peak of the plaque associated with SWE texture analysis parameters was created by a stepwise analysis, leading to a score with a sensitivity of 80% and a specificity of 78%. The ROC curve of this score found an AUC of 0.85 (Figure 2B). Conclusions Multiparameter scoring including plaque stiffness and flow analysis using ultrafast ultrasound imaging allows an effective identification of histologically vulnerable carotid plaques. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Fédération Française de cardiologieSociété Française de Cardiologie Figure 1Figure 2

Background Carotid plaque vulnerability assessment is an important factor in guiding the decision to treat significant carotid stenosis. Ultrafast Ultrasound Imaging (UF) offers the possibility of evaluating local flow velocities over an entire 2D image, allowing access to velocity measurements in contact with the arterial wall and to measure the wall shear stress (WSS). Purpose To evaluate the feasibility of WSS measurement in a prospective series of patients with carotid stenosis. Methods A 7.5 MHz linear probe of an Aixplorer scanner was used. UF acquisitions had 3 tilted plane waves transmits (−10; 0; 10°) and an effective frame rate of 5000Hz. We evaluated the flow velocity in 5 areas of the carotid wall: common carotid artery (1), plaque ascent (2), plaque peak (3), plaque descent (4), internal carotid artery (5) (Figure). WSS was computed with the vector field speed using the following formula, WSS=μ·δn·v with v the blood velocity, n the normal vector to the vessel wall and μ, the blood viscosity, calculated from the hematocrit value for each patient. WSS measurement method was first validated using a laminar flow phantom and known viscosity. And then, 33 patients were then prospectively evaluated, with a median carotid stenosis degree of 80% [75–85]. Results Significant correlation was found between in vitro measurement and the theoretical WSS values (R2=0.95; p Table 1 Wall's area Wall shear stress (Pa) Min Max Delta 1. Common carotid artery 0.14 [0.05–0.27] 1.55 [1.13–1.90] 0.73 [0.55–0.96] 2. Plaque's ascent 0.39 [0.24–0.59] 2.63 [1.89–3.28] 1.20 [0.89–1.79] 3. Plaque's peak 0.60 [0.32–0.89] 3.57 [2.47–4.45] 1.78 [1.44–2.46] 4. Plaque's descent 0.16 [0.13–0.22] 0.99 [0.80–1.32] 0.52 [0.34–0.73] 5. Internal carotid artery 0.17 [0.13–0.35] 1.37 [1.04–1.75] 0.72 [0.50–0.87] Results are median [25th–75th percentile]. Figure 1 Conclusion UF provide reliable WSS values. High WSS was present at the peak of the plaque, whereas lowest WSS values were found at the post-stenotic zone. WSS evaluation may help to better characterize the carotid plaque vulnerability.